本研究延伸朱(2008)探討的考慮需求不確定之單階層多製造廠產能規劃問題，在TFT-LCD產業面臨的市場需求具有劇烈波動的環境中，考量在規劃期間內，各產品族於各期有不同的需求分配，且前後期的需求之間存在關連性，欲達到淨利潤最大的目標下，決定各期各產品族最佳的產能分配決策，以及當現有產能無法滿足需求時，僅考量購買附屬設備─光罩來增加產品族於各廠區的產能，動態地決定各期最佳的產能擴充決策。
朱(2008)利用隨機動態規劃的後推歸納法(Backward Induction)進行求解，由於此方法計算和衡量每一個不同的狀態和決策組合，對於多廠區、多產品，甚至是多階層的產能規劃問題，在求解上的複雜度會呈指數的成長。相較於動態規劃的後推歸納法，本研究提出的近似動態規劃(Approximate Dynamic Programming Algorithm, ADP)結合蒙地卡羅模擬、Heuristics和前推歸納法(Forward Induction)，不需計算所有狀態執行各種決策的value function，透過蒙地卡羅模擬法決定可能的需求路徑(Demand Trajectories)，搭配Heuristics挑選較佳的決策，計算過程中各狀態的淨利潤值，以近似隨機動態規劃各狀態的績效值，以期能更有效率地得到最佳的產能分配結果與穩健的產能擴充計劃。
在與朱(2008)相同的實際產業案例環境中，驗證本研究提出的近似動態規劃模式，並與朱(2009)建構的隨機動態產能規劃模式結果進行比較，包含求解效率和姐的品質。更進一步加入朱(2009)未考量的因子─各產品於各期的需求情境不一致，在更大的狀態空間中，比較近似動態規劃模式與近似實務邏輯的滾動式線性規劃(Rolling Horizon MILP)的求解結果。另外，本研究同時探討近似動態規劃模式中，各參數對於求解結果的影響，包含需求路徑抽樣的數量、不同Heuristics和Bellman iteration收斂停止條件的訂定等。

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